Bridging the scale gap: enhancing point-scale rainfall estimates by post-processing ERA5

Pillosu, F., Hewson, T., Gascon, E., Vuckovic, M., Prudhomme, C. and Cloke, H. ORCID: https://orcid.org/0000-0002-1472-868X (2025) Bridging the scale gap: enhancing point-scale rainfall estimates by post-processing ERA5. ECMWF Technical Memoranda. 933. doi: 10.21957/e38fa17485

Abstract/Summary

Accurately estimating rainfall distributions, from small to extreme totals, is crucial for addressing various environmental challenges, including flood forecasting, water resource management, and disaster preparedness. Global Numerical Weather Prediction (NWP) models can provide useful rainfall estimates; yet, they often misrepresent point-scale observations from rain gauges, underestimating the frequency of small rainfall totals and underestimating extreme values. This study provides a systematic, global verification of four NWP-modelled rainfall datasets with different resolutions - ERA5’s Ensemble Data Assimilation (62 km, probabilistic), ERA5’s short-range forecasts (31 km, deterministic), short-range ECMWF reforecasts for cycle 46r1 (18 km, control run), and ERA5-ecPoint (point-scale, probabilistic) - against 20 years of point-rainfall observations from rain gauges around the world. The models’ ability to represent the entire rainfall distribution, including extreme rainfall, was assessed. Overall, the higher spatial resolution of NWP models enables a more accurate representation of gauge-based climatologies. Nonetheless, ERA5-ecPoint provides the most accurate representation, capturing the frequency of zeros, the growth rates of rainfall totals, and the wet tails more accurately. Moreover, due to its probabilistic nature, ERA5-ecPoint can estimate long return periods (e.g., 1000 years and more), offering insights into extremely rare or unprecedented events at specific locations. The model significantly improves performance in flat, hilly/mountainous regions. In very mountainous areas (e.g., the Andes), it underestimates zero rainfall totals and overestimates the length of the wet tails. These findings underscore the importance of using post-processing to enhance the local-scale validity of global NWP models. Moreover, as climate change intensifies extreme rainfall events, these findings are crucial for estimating accurate long-period rainfall climatologies, as needed for effective mitigation and resilience building, particularly in areas lacking comprehensive and reliable rain gauge records.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/128072
Identification Number/DOI	10.21957/e38fa17485
Refereed	Yes
Divisions	Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	ECMWF
Download/View statistics	View download statistics for this item